The disclosure generally relates to the field of fault detection, and more particularly to detecting, classifying, and diagnosing faults based on multi-device or multi-system transaction data.
Closely controlled testing and diagnostics are an integral part of hardware and software product development. Post-development (i.e., runtime) operation environments typically rely on client feedback to identify runtime errors or other problems. Such client feedback channels include network-accessible user feedback repositories in which error-specific information is provided in detailed messages. Client feedback may be effective for identifying device-specific and/or application-specific errors but is limited in terms of accurately identifying and/or diagnosing multi-component, multi-program issues. Furthermore, discrete client feedback information is frequently inadequate in identifying operational states and conditions that cause or otherwise precede potentially damaging results even in the absence of technical errors (e.g., code bug or hardware failure).
In some cases, infrastructure management (IM) systems may be utilized to detect system or program errors and to generate corresponding runtime errors messages. Similar to client feedback channels, however, monitoring agents deployed by IM systems typically provide systems and program operating data relating to discrete devices and subsystems. Conventional IM systems may lack the upper layer processing resources required to accurately assess vast, multi-directional input data that is generated by transactions between systems and/or applications across networks. Sensors or other open-platform network entities may provide real-time input of processed metric data generated from raw operational data captured by the sensors. However, such sensor-processed input data does not account for behavioral/functional changes in remote systems that may affect or be affected by local systems. Such localization of operational diagnostics may be inadequate in accounting for dynamic operational conditions of vast numbers of interconnected and multi-variable devices and systems.